Predictive Dynamics of Human Pain Perception

Guillermo A. Cecchi, Lejian Huang, Javeria Ali Hashmi, Marwan Baliki, María V. Centeno, Irina Rish, A. Vania Apkarian

Research output: Contribution to journalArticlepeer-review

53 Scopus citations


While the static magnitude of thermal pain perception has been shown to follow a power-law function of the temperature, its dynamical features have been largely overlooked. Due to the slow temporal experience of pain, multiple studies now show that the time evolution of its magnitude can be captured with continuous online ratings. Here we use such ratings to model quantitatively the temporal dynamics of thermal pain perception. We show that a differential equation captures the details of the temporal evolution in pain ratings in individual subjects for different stimulus pattern complexities, and also demonstrates strong predictive power to infer pain ratings, including readouts based only on brain functional images.

Original languageEnglish (US)
Article numbere1002719
JournalPLoS computational biology
Issue number10
StatePublished - Oct 2012

ASJC Scopus subject areas

  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Cellular and Molecular Neuroscience
  • Molecular Biology
  • Ecology
  • Computational Theory and Mathematics
  • Modeling and Simulation


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